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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Fawwad Hassan Jaskani

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Open access Sep 2026

SecurePrompt-IntegrityNet: Prompt-Injection-Resilient Data Integrity Verification for Agentic LLM Networks via Cryptographic Attestation and Activation Monitoring

Agentic large language model (LLM) networks are increasingly used in safety-critical settings where autonomous agents invoke tools, exchange context, and coordinate decisions. Prompt-injection attacks remain a significant threat to these multi-agent pipelines because they can compromise data flows between agents, bypass instruction hierarchies, and corrupt output integrity. Although defenses against injected prompts and mechanisms for cryptographically verifying model-related computations have been studied independently, no common framework unifies these complementary security perspectives in a protocol suitable for real-time agentic deployments. From the perspective of symmetry, secure inter-agent communication requires the preservation of an invariant integrity relationship between a message at its source and the corresponding message accepted at its destination. A benign communication path therefore exhibits a form of integrity symmetry, whereas prompt injection or message manipulation creates an asymmetric state in which the received payload, its semantic effect, or the receiving model’s internal activation pattern deviates from the trusted reference state. In this paper, we propose SecurePrompt-IntegrityNet (SPI-Net), a prompt-injection-resilient data integrity verification protocol that combines cryptographic attestation with anomaly-aware activation monitoring. SPI-Net provides three closely related mechanisms: a Merkle-tree-based commitment system that verifies the provenance and integrity of data payloads exchanged between agents; a layer-wise Mahalanobis-scoring Activation Anomaly Detector (AAD) that identifies distributional shifts in the intermediate representations of LLMs; and a Trust Propagation Consensus (TPC) mechanism that combines cryptographic and behavioral evidence into per-payload integrity verdicts. In this formulation, the Cryptographic Attestation Module (CAM) tests whether message-level structural symmetry is preserved between the sender and receiver, whereas the AAD detects behavioral symmetry breaking in activation space. Experiments on three multi-agent benchmarks under five adaptive attack strategies show that SPI-Net achieves a 96.8% detection rate with a 1.7% false positive rate, reduces the attack success rate by 94.3% relative to undefended baselines, verifies data integrity with 99.2% accuracy, and introduces only 38 ms of median per-message latency. These results demonstrate that jointly preserving cryptographic integrity symmetry and identifying activation-level asymmetry provides substantially stronger prompt-injection resilience than either verification mechanism alone.

Faisal Alhwikem, A. Khan, Fawwad Hassan Jaskani · 0 citations
Open access 2026

Teaching LLMs to Infer Real-World Consequences through Embodied Semantic Grounding

: Large Language Models (LLMs) have recently advanced in real-world commonsense reasoning, including understanding everyday object behaviors and inferring their attributes from text. However, they remain limited in reasoning about the real-world consequences of events, such as how object failures, obstructions, or structural changes affect the surrounding environment-especially without visual or sensorimotor input. Existing works like PIQA and NEWTON evaluate narrow sub-skills, such as whether an object action makes sense and whether object properties can be inferred, providing valuable benchmarks for commonsense and physical reasoning but offering limited evaluation of how events alter environmental functionality and downstream conditions. To address this gap, we propose Embodied Semantic Grounding (ESG), a framework that equips LLMs with consequence-aware text representations. ESG learns a consequence-grounded space by aligning event descriptions with affordance maps-structured representations of how an environment can be used or traversed after an event-capturing how structural changes modify environmental functionality. A Flan-T5-XL model is trained with a contrastive alignment objective to encode event descriptions into this space, for coherent prediction of consequences such as collapses, blockages, and environmental changes. Rather than introducing a new language-model architecture, ESG extends affordance-grounding with consequence-level representations of post-event environmental functionality. We evaluate ESG on a unified benchmark comprising PIQA, NEWTON, LIBERO-derived affordance text, and 2400 synthetic scenario-based tasks. Results show that ESG improves performance over baseline language models across commonsense reasoning and consequence-prediction benchmarks. Under structured affordance-map supervision, ESG improves zero-shot accuracy on PIQA and NEWTON and demonstrates improved performance on synthetic consequence-prediction scenarios designed to evaluate post-event environmental reasoning.

Manaswi Kulahara, Khadija Parwez, Faisal Alhwikem et al. · 0 citations

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